Visualization Of Genomic Data Research Articles

Integrating mixed reality technologies in genomic data visualization and analysis for bioinformatics research

With the advancement of Mixed Reality (MR) technologies and bioinformatics, researchers are exploring new ways to enhance the visualization and analysis of genomic data. The integration of MR technologies in bioinformatics research has the potential to revolutionize the way scientists interpret complex biological information. This article discusses the application of MR in genomic data visualization and analysis, highlighting its advantages in facilitating a more immersive and interactive experience. In particular, we will present case studies related to the implementation of the Unreal Engine in MR for bioinformatics research. As part of the research, the role of intellectual property in bioinformatics will be analyzed, providing insights into its significance and implications in the field. The integration of MR can improve collaboration among researchers and assist in the understanding of intricate patterns within genomic datasets. Furthermore, the article examines the challenges faced in implementing MR technologies in bioinformatics and addresses possible solutions to overcome these obstacles. Overall, the integration of MR in bioinformatics research has the potential to reshape the field and drive innovation in genomic data analysis.

AnVILWorkflow: A runnable workflow package for Cloud-implemented bioinformatics analysis pipelines.

Advancements in sequencing technologies and the development of new data collection methods produce large volumes of biological data. The Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) provides a cloud-based platform for democratizing access to large-scale genomics data and analysis tools. However, utilizing the full capabilities of AnVIL can be challenging for researchers without extensive bioinformatics expertise, especially for executing complex workflows. Here we present the AnVILWorkflow R package, which enables the convenient execution of bioinformatics workflows hosted on AnVIL directly from an R environment. AnVILWorkflowsimplifies the setup of the cloud computing environment, input data formatting, workflow submission, and retrieval of results through intuitive functions. We demonstrate the utility of AnVILWorkflowfor three use cases: bulk RNA-seq analysis with Salmon, metagenomics analysis with bioBakery, and digital pathology image processing with PathML. The key features of AnVILWorkflow include user-friendly browsing of available data and workflows, seamless integration of R and non-R tools within a reproducible analysis pipeline, and accessibility to scalable computing resources without direct management overhead. While some limitations exist around workflow customization, AnVILWorkflowlowers the barrier to taking advantage of AnVIL's resources, especially for exploratory analyses or bulk processing with established workflows. This empowers a broader community of researchers to leverage the latest genomics tools and datasets using familiar R syntax. This package is distributed through the Bioconductor project (https://bioconductor.org/packages/AnVILWorkflow), and the source code is available through GitHub (https://github.com/shbrief/AnVILWorkflow).

Minimax estimation of functional principal components from noisy discretized functional data

AbstractFunctional Principal Component Analysis is a reference method for dimension reduction of curve data. Its theoretical properties are now well understood in the simplified case where the sample curves are fully observed without noise. However, functional data are noisy and necessarily observed on a finite discretization grid. Common practice consists in smoothing the data and then to compute the functional estimates, but the impact of this denoising step on the procedure's statistical performance are rarely considered. Here we prove new convergence rates for functional principal component estimators. We introduce a double asymptotic framework: one corresponding to the sampling size and a second to the size of the grid. We prove that estimates based on projection onto histograms show optimal rates in a minimax sense. Theoretical results are illustrated on simulated data and the method is applied to the visualization of genomic data.

AnnoView enables large-scale analysis, comparison, and visualization of microbial gene neighborhoods.

The analysis and comparison of gene neighborhoods is a powerful approach for exploring microbial genome structure, function, and evolution. Although numerous tools exist for genome visualization and comparison, genome exploration across large genomic databases or user-generated datasets remains a challenge. Here, we introduce AnnoView, a web server designed for interactive exploration of gene neighborhoods across the bacterial and archaeal tree of life. Our server offers users the ability to identify, compare, and visualize gene neighborhoods of interest from 30 238 bacterial genomes and 1672 archaeal genomes, through integration with the comprehensive Genome Taxonomy Database and AnnoTree databases. Identified gene neighborhoods can be visualized using pre-computed functional annotations from different sources such as KEGG, Pfam and TIGRFAM, or clustered based on similarity. Alternatively, users can upload and explore their own custom genomic datasets in GBK, GFF or CSV format, or use AnnoView as a genome browser for relatively small genomes (e.g. viruses and plasmids). Ultimately, we anticipate that AnnoView will catalyze biological discovery by enabling user-friendly search, comparison, and visualization of genomic data. AnnoView is available at http://annoview.uwaterloo.ca.

Abstract 7406: Visualization and analysis of cancer genomics data using UCSC Xena

Abstract UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Researchers can easily view and explore public data, their own private data, or both using the Xena Browser. Private data are kept on the researcher's computer and are never uploaded to our public servers. We support Mac, Windows, and Linux. Questions Xena can help you answer:* Is overexpression of this gene associated with lower/higher survival?* What genes are differentially expressed between these two groups of samples?* What is the relationship between mutation, copy number, expression, etc for this gene? Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, GDC, PCAWG, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data: SNPs, INDELs, copy number variation, gene expression, ATAC-seq, DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotype information, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or using our URL links. Our signature Visual Spreadsheet view shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We also have dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, differential gene expression analysis, GSEA, charts, statistical analyses, genomic signatures, and the ability to generate URLs to live views. New features include: * Genome-wide differential gene expression analysis * GSEA analysis * Select samples directly from the screen for filtering and creating subgroups * Loading of Microsoft Excel files Our beta prototype site for visualizing single-cell data delivers million-cell-scale multi-omics data for interactive visualization in a web browser. Contact us for access to our beta prototype site. If you use us please cite our publication in Nature Biotechnology: https://www.nature.com/articles/s41587-020-0546-8 Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Visualization and analysis of cancer genomics data using UCSC Xena [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7406.

TBtools-II: A “one for all, all for one” bioinformatics platform for biological big-data mining

Since the official release of the stand-alone bioinformatics toolkit TBtools in 2020, its superior functionality in data analysis has been demonstrated by its widespread adoption by many thousands of users and references in more than 5000 academic articles. Now, TBtools is a commonly used tool in biological laboratories. Over the past 3 years, thanks to invaluable feedback and suggestions from numerous users, we have optimized and expanded the functionality of the toolkit, leading to the development of an upgraded version—TBtools-II. In this upgrade, we have incorporated over 100 new features, such as those for comparative genomics analysis, phylogenetic analysis, and data visualization. Meanwhile, to better meet the increasing needs of personalized data analysis, we have launched the plugin mode, which enables users to develop their own plugins and manage their selection, installation, and removal according to individual needs. To date, the plugin store has amassed over 50 plugins, with more than half of them being independently developed and contributed by TBtools users. These plugins offer a range of data analysis options including co-expression network analysis, single-cell data analysis, and bulked segregant analysis sequencing data analysis. Overall, TBtools is now transforming from a stand-alone software to a comprehensive bioinformatics platform of a vibrant and cooperative community in which users are also developers and contributors. By promoting the theme “one for all, all for one”, we believe that TBtools-II will greatly benefit more biological researchers in this big-data era.

Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal.

Enhanced cBioPortal features allow clinicians and researchers to effectively investigate longitudinal clinico-genomic data from patients with cancer, which will improve exploration of data from the AACR Project GENIE Biopharma Collaborative and similar datasets.

Unlocking the Potential of Genomic Data to Inform Typhoid Fever Control Policy: Supportive Resources for Genomic Data Generation, Analysis, and Visualization.

The global response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic demonstrated the value of timely and open sharing of genomic data with standardized metadata to facilitate monitoring of the emergence and spread of new variants. Here, we make the case for the value of Salmonella Typhi (S. Typhi) genomic data and demonstrate the utility of freely available platforms and services that support the generation, analysis, and visualization of S. Typhi genomic data on the African continent and more broadly by introducing the Africa Centres for Disease Control and Prevention's Pathogen Genomics Initiative, SEQAFRICA, Typhi Pathogenwatch, TyphiNET, and the Global Typhoid Genomics Consortium.

ChroKit: a Shiny-based framework for interactive analysis, visualization and integration of genomic data.

We developed ChroKit (the Chromatin toolKit), an interactive web-based framework written in R that enables intuitive exploration, multidimensional analyses, and visualization of genomic data from ChIP-Seq, DNAse-Seq or any other NGS experiment that reports the enrichment of aligned reads over genomic regions. This program takes preprocessed NGS data and performs operations on genomic regions of interest, including resetting their boundaries, their annotation based on proximity to genomic features, the association to gene ontologies, and signal enrichment calculations. Genomic regions can be further refined or subsetted by user-defined logical operations and unsupervised classification algorithms. ChroKit generates a full range of plots that are easily manipulated by point and click operations, thus allowing 'on the fly' re-analysis and fast exploration of the data. Working sessions can be exported for reproducibility, accountability, and easy sharing within the bioinformatics community. ChroKit is multiplatform and can be deployed on a server to enhance computational speed and provide simultaneous access by multiple users. ChroKit is a fast and intuitive genomic analysis tool suited for a wide range of users due to its architecture and its user-friendly graphical interface. ChroKit source code is available at https://github.com/ocroci/ChroKit and the Docker image at https://hub.docker.com/r/ocroci/chrokit.

Abstract 6635: Lipid and glycolipid antigen presentation faults as a mechanism of resistance to immune checkpoint therapy in poorly immunogenic melanomas

Abstract Introduction: Cold tumors are characterized by lacking infiltration of antitumor T cells. These tumors are universally refractory to immune checkpoint therapies (ICT). While poor T cell infiltration has been partly ascribed to low tumor mutational burden (TMB), TMB doesn’t necessarily exclude immunogenic responses nor ensure a response to ICT. Mechanisms of T cell exclusion in tumors with sufficient TMB may be caused by molecular failure of the antigen processing and presentation (APP), which impacts T cell generation. It was previously described that loss of heterozygosity (LOH) of an essential component of the MHC class I molecule, the beta-2-microglobulin (B2M), is associated with low antigen presentation, tumor T cell infiltration, and ICT resistance in melanoma. Here we provide further insight into APP and ICT response in melanoma. Methods: Transcriptomic analysis and visualization of genomic data from the GDC-TCGA skin cutaneous melanoma (SKCM) study were performed using the Xena platform. Four anti-PD1 metastatic melanoma cohorts were normalized with transcripts per million units. STRING protein-protein interaction analysis was performed in the human database with 11 nodes and 39 edges. Nanostring transcriptomic data from metastatic uveal melanoma (mUM) was revisited from our previous publications for Spearman’s correlational and group analysis of APP signatures amongst ICT responders and non-responders. Results: In an expanded analysis of 4 normalized anti-PD1 treated metastatic melanoma cohorts (n=272), we confirmed that transcriptomic levels of B2M are significantly reduced in non-responders. Moreover, STRING protein-protein interaction analysis of B2M revealed CD1D as a potential molecular partner of B2M. We, therefore, observed that CD1D transcriptomic expression is not only reduced in anti-PD1 poor responders but is a survival prognosticator in primary tumors from the GDC-TCGA study. Interestingly, CD1D expression is also significantly reduced in poorly immunogenic mUM tumors, a cancer type regarded as universally refractory to ICT. Conclusions: These findings suggest that APP faults in ICT-resistant tumors are not restricted to faults in peptide presentation to T lymphocytes (via B2M) but may also impact the presentation of tumor-associated lipid and glycolipid antigens via CD1d, which can impact antitumor responses of natural killer T cells (NKT). Adjuvant therapies that restore MHC-I and CD1d-mediated antigen presentation may unleash the response to ICT via NKT. Understanding CD1d expression faults locally and systemically may provide valuable prognostication information likely to impact how cancer is treated by optimally selecting patients for ICT and validating novel therapeutic targets for immunotherapy development. Citation Format: Carlos Rogerio Figueiredo. Lipid and glycolipid antigen presentation faults as a mechanism of resistance to immune checkpoint therapy in poorly immunogenic melanomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6635.

Abstract 2066: Visualization and analysis of cancer genomics data using UCSC Xena

Abstract UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Researchers can easily view and explore public data, their own private data, or both using the Xena Browser. Private data are kept on the researcher's computer and are never uploaded to our public servers. We support Mac, Windows, and Linux. Questions Xena can help you answer: * Is overexpression of this gene associated with lower/higher survival? * What genes are differentially expressed between these two groups of samples? * What is the relationship between mutation, copy number, expression, etc for this gene? Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, GDC, PCAWG, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data: SNPs, INDELs, copy number variation, gene expression, ATAC-seq, DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotype information, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or using our URL links. Our signature Visual Spreadsheet view shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We also have dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, differential gene expression analysis, charts, statistical analyses, genomic signatures, and the ability to generate URLs to live views. We link out to the UCSC Genome Browser as well as MuPIT/CRAVAT and TumorMap. New features include: * Genome-wide differential gene expression analysis * Select samples directly from the screen for filtering and creating subgroups * Violin plots on any numerical data * Loading of Microsoft Excel files Our beta prototype site for visualizing single-cell data delivers million-cell-scale multi-omics data for interactive visualization in a web browser. Contact us for access to our beta prototype site. If you use us please cite our publication in Nature Biotechnology: https://www.nature.com/articles/s41587-020-0546-8 Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Visualization and analysis of cancer genomics data using UCSC Xena [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2066.

National Human Genome Research Institute Genomic Data Science Analysis, Visualization, and Informatics Lab-Space: Reaching out to Clinicians.

National Human Genome Research Institute Genomic Data Science Analysis, Visualization, and Informatics Lab-Space: Reaching out to Clinicians.

WAVES (Web-based tool for Analysis and Visualization of Environmental Samples)-a web application for visualization of wastewater pathogen sequencing results.

Environmental monitoring of pathogens provides an accurate and timely source of information for public health authorities and policymakers. In the last 2 years, wastewater sequencing proved to be an effective way of detection and quantification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants circulating in population. Wastewater sequencing produces substantial amounts of geographical and genomic data. Proper visualization of spatial and temporal patterns in these data is crucial for the assessment of the epidemiological situation and forecasting. Here, we present a web-based dashboard application for the visualization and analysis of data obtained from sequencing of environmental samples. The dashboard provides multi-layered visualization of geographical and genomic data. It allows to display frequencies of detected pathogen variants as well as individual mutation frequencies. The features of WAVES (Web-based tool for Analysis and Visualization of Environmental Samples) for early tracking and detection of novel variants in the wastewater are demonstrated in an example of BA.1 variant and the signature Spike mutation S: E484A. WAVES dashboard is easily customized through the editable configuration file and can be used for different types of pathogens and environmental samples. WAVES source code is freely available at https://github.com/ptriska/WavesDash under MIT license. A demo version of this application can be accessed at: https://wavesdashboard.azurewebsites.net/.

SiVAE: interpretable deep generative models for single-cell transcriptomes

Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding dimension. We present siVAE, a VAE that is interpretable by design, thereby enhancing downstream analysis tasks. Through interpretation, siVAE also identifies gene modules and hubs without explicit gene network inference. We use siVAE to identify gene modules whose connectivity is associated with diverse phenotypes such as iPSC neuronal differentiation efficiency and dementia, showcasing the wide applicability of interpretable generative models for genomic data analysis.

Medication recommendation in cancer treatment based on cell line similarity

The possibility of performing the sequencing of the human genome has revolutionized the field of biology and medicine itself, paving the way to personalized medicine. In the case of cancer, which is a disease caused by mutations in DNA, the study of genome brings some advantages, because through the study of the functioning of the human organism and the mutations caused by tumor, this makes it possible to explore the effectiveness of a significant number of treatments through medication for certain cell lines derived from various oncological problems. Bioinformatics has some powerful tools that help in the acquisition, storage, analysis, and visualization of genomic data. The research underlying this article was based on the analysis of biological data and established as its main objective the test of the premise that the most effective medications for a particular cell line are those that have been shown to be most effective in cell lines that are visually similar. A sample of 100 images of cell lines was used, and the medications used on each cell line were compared and the above premise was validated, concluding that the similarity in DNA between cell lines allows similar treatments to be recommend.

Seven clusters of data visualization articles in Scopus using social network analysis

The aim of this study was to analyse the bibliographic characteristics and content of articles on Data Visualization published in journals indexed by Scopus written by researchers from throughout the world. We conducted a bibliometric and content analysis of publication in the Scopus database. We only retrieved articles written in English. We conducted content analysis using the VOSviewer software and visualized the co-occurrence of keywords and bibliographic coupling of sources and countries. Following the study protocol, we found 862 articles on Data Visualization over the past 30 years. The most productive journal that published these articles was Lecture Notes In Computer Science (n=32). The most productive country was the United States (n=305). Based on citations, the most influential authors, and journals were Thorvaldsdóttir et al., (2013) [Thorvaldsdóttir, H., Robinson, J. T., & Mesirov, J. P. (2013). Integrative Genomics Viewer (IGV): High-performance genomics data visualization and exploration. Briefings in Bioinformatics, 14(2), 178–192.] (n=4699), and IEE Transactions on Visualization and Computer Graphics (n=656). The keywords of research on Data Visualization formed 7 clusters (e.g. Data Visualization, Visualization, and Human). From a global perspective, Data Visualization research in the past 30 years has increased significantly. There were European published journals nominated publications. Thus, Asian countries need to conduct more active research on this topic.

DivBrowse-interactive visualization and exploratory data analysis of variant call matrices.

The sequencing of whole genomes is becoming increasingly affordable. In this context, large-scale sequencing projects are generating ever larger datasets of species-specific genomic diversity. As a consequence, more and more genomic data need to be made easily accessible and analyzable to the scientific community. We present DivBrowse, a web application for interactive visualization and exploratory analysis of genomic diversity data stored in Variant Call Format (VCF) files of any size. By seamlessly combining BLAST as an entry point together with interactive data analysis features such as principal component analysis in one graphical user interface, DivBrowse provides a novel and unique set of exploratory data analysis capabilities for genomic biodiversity datasets. The capability to integrate DivBrowse into existing web applications supports interoperability between different web applications. Built-in interactive computation of principal component analysis allows users to perform ad hoc analysis of the population structure based on specific genetic elements such as genes and exons. Data interoperability is supported by the ability to export genomic diversity data in VCF and General Feature Format 3 files. DivBrowse offers a novel approach for interactive visualization and analysis of genomic diversity data and optionally also gene annotation data by including features like interactive calculation of variant frequencies and principal component analysis. The use of established standard file formats for data input supports interoperability and seamless deployment of application instances based on the data output of established bioinformatics pipelines.

Abstract 5039: Visualization and analysis of cancer genomics data using UCSC Xena

Abstract UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Researchers can easily view and explore public data, their own private data, or both using the Xena Browser. Private data are kept on the researcher's computer and are never uploaded to our public servers. The toll supports Mac, Windows, and Linux. Questions Xena can help you answer: 1) Is overexpression of this gene associated with lower/higher survival? 2) What genes are differentially expressed between these two groups of samples? 3) What is the relationship between mutation, copy number, expression, etc for this gene? Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, GDC, PCAWG, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data: SNPs, INDELs, copy number variation, gene expression, ATAC-seq, DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression, and structural variants. We also support clinical data such as phenotype information, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or using our URL links. Our signature Visual Spreadsheet view shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We also have dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, differential gene expression analysis, charts, statistical analyses, genomic signatures, and the ability to generate URLs to live views. We link out to the UCSC Genome Browser as well as MuPIT/CRAVAT and TumorMap. New features include: - Genome-wide differential gene expression analysis - New interface for filtering samples and creating subgroups - New interface to create charts and graphs - Violin plots on any numerical data - Loading of Microsoft Excel files - A new Publication Page showcasing publications and authors that use UCSC Xena Our beta prototype for visualizing single-cell data delivers million-cell-scale multi-omics data for interactive visualization in a web browser. Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Visualization and analysis of cancer genomics data using UCSC Xena [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5039.

Abstract 1201: GenomeCruzer: A tool for interactive 3D visualization and analysis of multidimensional cancer omics

Abstract In cancer genomics, integrative analysis of data obtained from biological samples of patient cohorts requires handling large groups of patients, each with molecular data of different type for thousands of genes, such as expression, mutation, copy number, and others. Currently available tools allow visualization and analysis of such complex data, however forcing the user to work with partial views due to the amount of information. Also, these systems usually involve somewhat laborious procedures, so that for a complete analysis of the information it is often necessary to use many tools in sequence. Here we present GenomeCruzer, a software for 3D interactive visualization and analysis of multidimensional omic data sets (e.g. gene expression, methylation, copy number alteration). GenomeCruzer is particularly useful to quickly achieve clinical and biological insight, providing easy integration and interactive visualization of genomic data of any kind. GenomeCruzer has the dual objectives of representing large amounts of genomic data in an easily readable way, allowing interactive analysis and providing visual results in real-time. Powerful graphics also allow simultaneous display of different types of values for the same objects, through multiple dimensions and customizable visual metaphors. The user can navigate this 3D environment and interact with the different elements, which constitute a representation of the starting datasets, building analysis paths and using statistical tools e.g. for the comparison of groups to highlight correlations. In conclusion, GenomeCruzer enables users without bioinformatics background to browse, analyse and interpret complex omics datasets for multiple groups of patients and genes simultaneously, extracting clinical correlates and biological knowledge. Citation Format: Claudio Isella, Riccardo Corsi, Jamal Elhasnaoui, Andrea Bertotti, Luca Vezzadini, Enzo Medico. GenomeCruzer: A tool for interactive 3D visualization and analysis of multidimensional cancer omics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1201.

Plotgardener: cultivating precise multi-panel figures in R.

The R programming language is one of the most widely used programming languages for transforming raw genomic datasets into meaningful biological conclusions through analysis and visualization, which has been largely facilitated by infrastructure and tools developed by the Bioconductor project. However, existing plotting packages rely on relative positioning and sizing of plots, which is often sufficient for exploratory analysis but is poorly suited for the creation of publication-quality multi-panel images inherent to scientific manuscript preparation. We present plotgardener, a coordinate-based genomic data visualization package that offers a new paradigm for multi-plot figure generation in R. Plotgardener allows precise, programmatic control over the placement, esthetics and arrangements of plots while maximizing user experience through fast and memory-efficient data access, support for a wide variety of data and file types, and tight integration with the Bioconductor environment. Plotgardener also allows precise placement and sizing of ggplot2 plots, making it an invaluable tool for R users and data scientists from virtually any discipline. Package: https://bioconductor.org/packages/plotgardener, Code: https://github.com/PhanstielLab/plotgardener, Documentation: https://phanstiellab.github.io/plotgardener/. Supplementary data are available at Bioinformatics online.